Reliability Analysis of Electrical System of CNC Machine Tool Based on Dynamic Fault Tree Analysis Method

The electrical system of CNC machine tool is very complex which involves many uncertain factors and dynamic stochastic characteristics when failure occurs.Therefore,the traditional system reliability analysis method,fault tree analysis(FTA)method,based on static logic and static failure mechanism is no longer applicable for dynamic systems reliability analysis.Dynamic fault tree(DFT)analysis method can solve this problem effectively.In this method,DFT first should be pretreated to get a simplified fault tree(FT);then the FT was modularized to get the independent static subtrees and dynamic subtrees.Binary decision diagram(BDD)analysis method was used to analyze static subtrees,while an approximation algorithm was used to deal with dynamic subtrees.When the scale of each subtree is smaller than the system scale,the analysis efficiency can be improved significantly.At last,the usefulness of this DFT analysis method was proved by applying it to analyzing the reliability of electrical system.

The Dynamic-to-Static Conversion of Dynamic Fault Trees Using Stochastic Dependency Graphs and Stochastic Activity Networks

In this paper a new modeling framework for the dependability analysis of complex systems is presented and related to dynamic fault trees (DFTs). The methodology is based on a modular approach: two separate models are used to handle, the fault logic and the stochastic dependencies of the system. Thus, the fault schema, free of any dependency logic, can be easily evaluated, while the dependency schema allows the modeler to design new kind of non-trivial dependencies not easily caught by the traditional holistic methodologies. Moreover, the use of a dependency schema allows building a pure behavioral model that can be used for various kinds of dependability studies. In the paper is shown how to build and integrate the two modular models and convert them in a Stochastic Activity Network. Furthermore, based on the construction of the schema that embeds the stochastic dependencies, the procedure to convert DFTs into static fault trees is shown, allowing the resolution of DFTs in a very efficient way.

Study on System Failure Probability Model Based on Dynamic Fault Tree

Aiming at the characteristics of complex logic relation and multiple dynamic gates in system,its failure probability model is established based on dynamic fault tree. For the multi-state dynamic fault tree,it can be transferred into Markov chain with continuous parameters. The state transfer diagram can be decomposed into several state transfer chains,and the failure probability models can be derived according to the lengths of the chains. Then,the failure probability of the dynamic fault tree analysis(DFTA) can be obtained by adding each chain's probability. The failure probability calculation of DFTA based on the continuous parameter Markov chain is proposed and proved. Given an example,the analytic method is compared with the conventional methods which have to solve the differential equation. It is known from the results that the analytic method can be applied to engineering easily.

Dynamic Fault Tree Analysis for Explosive Logic Network with Two-Input-One-Output

The explosive logic network( ELN) with two-input-oneoutput was designed with three explosive logic gap null gates. The time window of the output of the ELN was given,after which the dynamic fault tree analysis was implemented. Two dynamic failure modes of the ELN were obtained,and then their own Markov transition processes were established. After that,the probability of failure was calculated from the corresponding state transition diagram. The reliability of the ELN which was in different length of time under the ambient incentive was then analyzed. Based on the above processing,the reliability of the ELN can be improved.

Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant

Fault diagnostics is important for safe operation of nuclear power plants(NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neurofuzzy approaches, support vector machine, K-nearest neighbor classifiers and inference methodologies. Among these methods, dynamic uncertain causality graph(DUCG)has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is complicate and, in many cases, results redundant on the symptoms needed to correctly classify the fault. In this paper, we propose a method to simplify causal graph construction in an automatic way. The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree(FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT. Genetic algorithm(GA) is, then, used for the optimization of the FDT, by performing a wrapper search around the FDT: the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system. The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation. The results show that the FDT, with GA-optimized symptoms and diagnosis strategy, can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.

基于TREE-LSTM算法的船舶汽轮机组变负荷故障诊断

针对船舶汽轮机组变负荷过程故障诊断中的耦合参数时序特征难以捕捉以及正常参数变动的干扰等问题,引入TREE-LSTM神经网络模型以实现复杂非线性系统动态数据分类。首先建立某船舶汽轮机组仿真模型,分析并进行故障仿真;随后进行数据预处理与特征工程;最后训练TREE-LSTM模型进行故障诊断,并与SVM、LSTM等模型进行比较。TREE-LSTM模型对于船舶汽轮机组变负荷过程的故障诊断正确率为98.7%,正确率最高。由于引入时间序列与复杂神经网络拓扑结构,TREE-LSTM在处理非线性系统动态数据分类问题时效果更好。

Modular solution of dynamic multiple-phased systems

A new modular solution to the state explosion problem caused by the Markov-based modular solution of dynamic multiple-phased systems is proposed. First, the solution makes full use of the static parts of dynamic multiple-phased systems and constructs cross-phase dynamic modules by combining the dynamic modules of phase fault trees. Secondly, the system binary decision diagram （BDD） from a modularized multiple- phased system （MPS）is generated by using variable ordering and BDD operations. The computational formulations of the BDD node event probability are derived for various node links and the system reliability results are figured out. Finally, a hypothetical multiple-phased system is given to demonstrate the advantages of the dynamic modular solution when the Markov state space and the size of the system BDD are reduced.

Reliability Analysis for Complex Systems Based on Dynamic Evidential Network Considering Epistemic Uncertainty

Fault tolerant technology has greatly improved the reliability of modern systems on one hand and makes their failure mechanisms more complex on the other.The characteristics of dynamics of failure,diversity of distribution and epistemic uncertainty always exist in these systems,which increase the challenges in the reliability assessment of these systems significantly.This paper presents a novel reliability analysis framework for complex systems within which the failure rates of components are expressed in interval numbers.Specifically,it uses a dynamic fault tree(DFT)to model the dynamic fault behaviors and copes with the epistemic uncertainty using Dempster-Shafer(D-S)theory and interval numbers.Furthermore,an approach is presented to convert a DFT into a dynamic evidential network(DEN)to calculate the reliability parameters.Additionally,a sorting method based on the possibility degree is proposed to rank the importance of components represented by interval numbers in order to obtain the most critical components,which can be used to provide the guidance for system design,maintenance planning and fault diagnosis.Finally,a numerical example is provided to illustrate the availability and efficiency of the proposed method.

激光自适应光学系统可靠性改进效果评估

随着自适应光学技术在激光领域的发展,工程上以经典自适应光学(AO)系统为基础,增加了多种基于软件监测和硬件保护的改进措施以保证激光AO系统稳定连续出光。面对结构复杂度提升带来的可靠性挑战,如何构建系统失效模型对激光AO系统可靠性进行评估,成为影响激光AO系统发展的重要一环。本文以激光光稳净化AO系统为例,提出使用动态故障树方法对激光AO系统可靠性进行评估,根据设备间动态关系建立动态故障树(DFT),结合厂家信息、疲劳寿命试验与历史数据估计得到底事件失效率,使用二元决策图和马尔可夫模型求解得到DFT的可靠性参数。使用DFT分析增加改进措施的AO系统可靠运行时间,结果相对于基本故障树获得了十倍以上的提高。实际系统调试期间,在预计的可靠运行时间内未发生自因故障,与DFT估计结果一致。验证了应用DFT方法评估增加改进措施后的激光AO系统可靠性更准确。

基于动态故障树的交通信号灯监测系统故障分析

针对交通灯故障无法及时处理影响交通效率与安全的问题,设计了基于ZigBee技术的监测终端,根据监测系统中部件发生故障的时序关系建立动态故障树模型,分别采用二元决策图法BDD(binary decision diagrams)和马尔科夫模型对静态子树和动态子树进行定量分析,确定监测系统底事件对系统整体的概率重要度,计算得到导致交通灯故障的主要原因为光纤传感器位置移动和灯珠寿命到期。使用动态故障树法设计交通灯监测系统为工作人员快速分析故障原因提供了理论依据。

基于动态贝叶斯网络的蒽醌法制备双氧水工艺风险评估

为降低因蒽醌法制备双氧水而引起的火灾爆炸风险,从蒽醌法制备双氧水工艺中选取危险性较大的萃取净化工序展开风险评估。首先,分析该工序的危险源,采用Freefta软件建立事故树模型,在该模型的基础上使用GeNIe软件绘制事故树转换的动态贝叶斯网络模型;然后,运用专家打分法和模糊分析法计算模型中基本事件的先验概率,运用GeNIe软件在设定的前置条件下计算模型中基本事件的后验概率;最后,比较先验、后验概率之间的变化幅度,确定重要基本事件,揭示引起火灾爆炸的危险源并制定应急处置技术。结果表明:降解物与杂质造成污染、工艺副反应产生并增多、催化剂失效以及双氧水自身化学性质活跃4种重要危险源对萃取净化工序火灾爆炸影响最大;从阻止火灾蔓延与液体疏通的角度针对重要危险源制定应急处置技术效果较好。

基于T-S动态故障树的桁架机器人系统可靠性分析

为了提高桁架机器人系统的动态可靠性,并且在发生故障时提升排查故障的效率,提出一种对桁架机器人系统动态可靠性分析的方法。首先,对桁架机器人系统建立T-S动态故障树;然后,把T-S动态故障树和T-S动态门规则分别转化为离散时间贝叶斯网络(DTBN)与相应网络节点的条件概率表,根据DTBN正反向推理分别计算得到各时间段以及任务时间内系统失效概率和根节点后验概率,并获得各根节点的T-S概率重要度、T-S关键重要度和灵敏度;最后,应用Monte Carlo仿真法进行验证,结果显示相对误差为0.29%,证明所提方法可行。给出当系统发生故障时应优先排查的零部件,并确定了系统薄弱环节,得出应优先提高可靠度的零部件,为桁架机器人系统的动态可靠性分析提供了理论依据。

基于融合系统方法的飞机复杂系统安全性分析

面对日益复杂的飞机系统,传统的安全性分析方法对复杂系统间的不安全交互行为和危险源的识别能力不足。为有效评价持续适航阶段的飞机系统安全性,提出了一种融合系统理论过程分析(system theory process analysis,STPA)和动态故障树(dynamic fault tree,DFT)的改进的STPA安全性分析方法和评价模型。模型采用STPA识别出不安全控制行为和致因因素,并将其与动态故障树分析方法相融合,以事故致因理论优化致因分析方法,计算得出不安全控制行为发生概率并确定系统潜在危险的关键致因因素。以飞机起落架系统为例进行分析验证,结果表明,改进后的STPA分析方法可以准确地对系统危险进行识别和分析,为持续适航阶段的安全性分析提供支持。

基于离散时间贝叶斯网络的CBTC车载设备可靠性分析

基于通信的列车控制系统(CBTC)车载设备用于实现列车安全防护和自动运行,其可靠性直接关系到列车的运行安全和效率。针对设备运营过程中的动态失效问题,基于离散时间贝叶斯网络(DTBN)对CBTC车载设备可靠性进行分析。首先,在分析CBTC车载设备功能结构的基础上构建系统动态故障树模型,基于动态故障树模型与DTBN模型的转换规则以及DTBN逻辑门条件概率表,建立基于DTBN的可靠性分析模型;其次,通过DTBN的正向推理实现CBTC车载设备的可靠度评估;最后,通过DTBN的反向推理及重要度计算,实现CBTC车载设备薄弱环节分析及重要度排序。结果表明:ATP子系统中输出继电器板、输出板等是CBTC车载设备最薄弱的板卡级设备,需重点关注。该方法能为CBTC车载设备的可靠性分析提供依据,有效提升系统智能运维水平。

基于DTBN与T-S故障树的电池热管理系统可靠性分析

提出一种评估电动汽车动力电池热管理系统的可靠性分析算法。构建了电池热管理系统T-S动态故障树,转化为离散时间Bayes网络(DTBN)模型。同时将T-S动态门规则转化为网络节点的条件概率表。依据所建可靠性模型和部件的故障率,计算得到动力电池热管理系统在任务时间内的故障概率值为0.453,并获得各部件的后验概率、概率重要度和关键重要度。结果表明:对比Monte Carlo仿真方法,本方法的故障概率计算值误差小于5%,概率重要度靠前的是单体电池温度传感器、电池冷却液泵、电池冷却液管路等部件。该方法能克服传统故障树分析难以构建Bayes网络条件概率表等问题。

基于动态监测与事故树分析的超大深基坑风险分析方法研究

为对城市特大交通枢纽超大深基坑工程进行动态风险等级定量分析,提出风险事故树与基坑动态监测数据相结合的超大深基坑工程风险分析方法。列举基坑工程典型的各类风险源,绘制风险事故树并分析其基本事件结构重要度,将风险影响程度高的监测项目作为风险动态评价指标,同时考虑监测项目的动态评价因素和静态评价因素,将主因素突出法和综合评价分析法相结合。方法应用于北京城市副中心站特大交通枢纽超大深基坑工程项目中。研究结果表明:可有效避免获取事故树底事件发生概率的困难和单一使用综合评价分析法对基坑风险评价的失真影响,在实际工程中能够得到合理的基坑风险等级,具有较好的实用性和可操作性。研究结果可为超大深基坑工程风险分析提供参考。

基于动态故障树联合动态Bayes网络的汽轮机跳机电磁阀配电系统可靠性研究

跳机电磁阀配电系统可靠性关系到机组安全运行。针对跳机电磁阀配电系统中因电源切换易造成跳机电磁阀误动问题,分析了误动及遮断系统失效原理,从取消电源切换装置和改变供电拓扑结构两方面考虑,提出两种配电系统改进方案,并采用动态故障树蒙特卡洛联合动态Bayes网络对跳机电磁阀配电系统进行可靠性研究。仿真结果表明,所提的改进方案不仅解决了电源切换引起电磁阀误动问题,提高了供电稳定性,而且系统具有较高的可用率。

船舶压载水系统可靠性分析

压载水系统能过保证船舶在航行过程中船体稳定,是邮轮重要的组成部分。由于压载水系统结构复杂,系统可靠性难以分析。为了分析压载水系统的可靠性变化,考虑系统在连续时间下的状态变化,基于连续贝叶斯网络,建立了一种新的船舶压载水系统可靠性分析方法。首先基于压载水系统的故障机理,提出动态故障树模型。然后利用单位阶跃函数和冲激函数,将动态故障树模型转化为连续时间的贝叶斯网络,对系统可靠性进行分析,最后,利用算例对船舶压载水系统进行仿真,得到船舶压载水系统的可靠性以及剩余寿命随时间变化的曲线。

基于动态故障树的汽车系统故障诊断方法

为了提高汽车故障诊断效率,降低诊断代价并解决静态故障树(SFT)无法描述汽车系统中动态失效序列的问题,提出一种基于动态故障树(DFT)的汽车系统故障诊断方法。首先,利用DFT建立系统的失效模式模型,并采用顺序二元决策图(SBDD)计算DFT的最小割集(MCS);然后计算MCS以及MCS中组件的诊断重要度(DIF);并在此基础上给出基于统一诊断服务(UDS)协议的汽车故障诊断方法,将诊断故障码对应的MCS过滤并排序,优先诊断DIF大的MCS和系统组件;最后通过一个汽车主动转向系统的应用实例对本文方法的有效性进行验证。案例分析结果表明,所提方法能够计算汽车故障诊断MCS序列,可以有效指导汽车故障诊断工作。

基于动态故障树的铸造起重机起升机构可靠性分析

针对铸造起重机起升机构可靠性问题,提出一种基于动态故障树的起升机构可靠性分析方法。首先介绍动态故障树理论与动态故障门,对铸造起重机进行动态故障树建模,通过线性搜寻法对动态故障树进行分层,得到其动态子树和静态子树,采用马尔可夫链和二元决策图相结合的方法对子树模块进行求解,最后综合各个子树故障率得到起升机构故障率。